Some context in the era of Linked Data

Introduction Knowledge Graphs (KGs) are currently on the rise. In their latest Hype Cycle for Artificial Intelligence (2018), Gartner highlighted: “The rising role of content and context for delivering insights with AI technologies, as well as recent knowledge graph offerings for AI applications have pulled knowledge graphs to the surface.” We can roughly di […]

Knowledge graphs are essential for any information architecture built upon semantics and AI. The Linked Data Life Cycle provides guideline for data governance within the semantic web framework. The post Knowledge Graphs – Connecting the Dots in an Increasingly Complex World appeared first on Semantic Web Company.

With the UN predicting that more than 70 percent of the world’s population will live in urban areas by 2050, the development of sustainable smart cities is a rising need. Cities are now capable of collecting and analyzing enormous amounts of data to automate processes, improve service quality, and to make better decisions. This opens ... The post How Semanti […]

Drupal is one of the favourite enterprise content management systems. Especially government and non-governmental organizations embrace this open source platform to build advanced digital experiences. Over the last years, we have been developing several PoolParty semantic technology features and modules that integrate natively into Drupal. In this blog post, […]

In our recent endeavor to import in PoolParty the Google Product taxonomy in different languages, we encountered some challenges that needed to be addressed. The first challenge was that the Google Product taxonomy is in Excel (XLS) format, and for each language there is a separate file. The second challenge is on how to align ... The post Data wrangling wit […]

Tag Archives for metadata

Search terms are like keys to documents or to any other type of content. In our days, not only the volume of available documents is increasing rapidly, but also the size of the keychain is growing.

Before we start to discuss how to organize/index the documents themselves, we should first talk about methods to organize the keys.

Take a look at these three approaches:

On the left-hand side you can see a nice example of implicit semantics. The old receptionist knows exactly which key fits into which lock. This knowledge is implicit and can be remembered by the shapes of the keys. Before the old receptionist has retired and the knowledge would have gone, labels have been attached to every key. The semantics of the keys has become more explicit, but this is still quite ambiguous (as visualised in the center column). There is no index of all the labels and colours being used, neither an explicit methodology how new keys should be labeled. As the hotel grows, the labeling system becomes quickly a mess. On the right-hand side the solution for this problem is offered: Not only the semantics of the keys becomes more explicit, but also the semantics of the semantics. For instance, the position of a key represents the position of the room, which can be unlocked by this. The number of the row of the key cabinet represents the storey of the room being unlocked, etc.

This methodology in order to organise keys helps to orientate and to remember; it can be explained with low effort to any new receptionist and it can be scaled-up in case your hotel should grow in the next few years.

Most information professionals already know: separation of content and presentation helps to manage and deliver complex information. This can only be done by using enriched structured content. Some call this intelligent content.

But why exactly is metadata per document (some call it “tagging”) not enough?

Here is a very brief slide-deck, which explains the difference between the traditional approach and the graph-based approach to develop not only a metadata layer seperated from the content layer, but also a knowledge layer on top of it.

PoolParty 2.7 offers new and comfortable ways to enrich any SKOS thesaurus with additional facts from the semantic web (see: LOD cloud). This functionality (which was extended significantly with version 2.7 in June 2010) supports any thesaurus manager to generate much richer knowledge models (ontologies) around specific domains than ever before (without facing high extra costs due to additional research). There are at least three arguments why one should consider building such “extended thesauri”:

Use even more metadata to describe your resources and improve navigation and semantic search functionalities significantly

Use linked data for data integration and semantic mashups; combine your own contents with contents from the web to improve your business intelligence

A short example: Just imagine you are working on a knowledge model in the area of “Skiing in Austria”. You have stated that one of Tyrols´s (most famous) skiing areas is “Kitzbühel“. By looking up at geonames.org you get extra metadata, e.g. alternate labels like “Kitzbichl” or longitude and latitude to display the concept on a map. In a next step you add famous Austrian skiers like “Hermann Maier” and “Franz Klammer“. From DBpedia you retrieve additional category information like Maier is a “Person born in 1972“, thumbnail pictures and also some links to other linked data sources, e.g. to the New York Times. Here we can learn that the NYT has mentioned Hermann Maier in 14 articles already. Finally we can add “Toni Sailer” as a third skier and we will find out by harvesting linked data that he was born in Tyrol and therefore we can add a new relation in our thesaurus between him and Tyrol.

We have learned: Linked Data can help us to build expressive knowledge models by using SKOS as an “interface” to the Linked Data Cloud.